Self-Driving Car Engineer Nanodegree

Deep Learning

Project: Build a Traffic Sign Recognition Classifier

In this notebook, a template is provided for you to implement your functionality in stages, which is required to successfully complete this project. If additional code is required that cannot be included in the notebook, be sure that the Python code is successfully imported and included in your submission if necessary.

Note: Once you have completed all of the code implementations, you need to finalize your work by exporting the iPython Notebook as an HTML document. Before exporting the notebook to html, all of the code cells need to have been run so that reviewers can see the final implementation and output. You can then export the notebook by using the menu above and navigating to \n", "File -> Download as -> HTML (.html). Include the finished document along with this notebook as your submission.

In addition to implementing code, there is a writeup to complete. The writeup should be completed in a separate file, which can be either a markdown file or a pdf document. There is a write up template that can be used to guide the writing process. Completing the code template and writeup template will cover all of the rubric points for this project.

The rubric contains "Stand Out Suggestions" for enhancing the project beyond the minimum requirements. The stand out suggestions are optional. If you decide to pursue the "stand out suggestions", you can include the code in this Ipython notebook and also discuss the results in the writeup file.

Note: Code and Markdown cells can be executed using the Shift + Enter keyboard shortcut. In addition, Markdown cells can be edited by typically double-clicking the cell to enter edit mode.


Step 0: Load The Data

In [1]:
# Load pickled data
import pickle

# TODO: Fill this in based on where you saved the training and testing data
training_file = './TrafficSigns/train.p'
testing_file = './TrafficSigns/test.p'

with open(training_file, mode='rb') as f:
    train = pickle.load(f)
with open(testing_file, mode='rb') as f:
    test = pickle.load(f)
        
X_train, y_train = train['features'], train['labels']
X_test,   y_test =  test['features'],  test['labels']

# Debug info
print()
print("(debug) Size of Training & Validation data:",len(X_train))
print("(debug) Size of Test data:                 ",len(X_test))
(debug) Size of Training & Validation data: 39209
(debug) Size of Test data:                  12630

Step 1: Dataset Summary & Exploration

The pickled data is a dictionary with 4 key/value pairs:

  • 'features' is a 4D array containing raw pixel data of the traffic sign images, (num examples, width, height, channels).
  • 'labels' is a 1D array containing the label/class id of the traffic sign. The file signnames.csv contains id -> name mappings for each id.
  • 'sizes' is a list containing tuples, (width, height) representing the original width and height the image.
  • 'coords' is a list containing tuples, (x1, y1, x2, y2) representing coordinates of a bounding box around the sign in the image. THESE COORDINATES ASSUME THE ORIGINAL IMAGE. THE PICKLED DATA CONTAINS RESIZED VERSIONS (32 by 32) OF THESE IMAGES

Complete the basic data summary below. Use python, numpy and/or pandas methods to calculate the data summary rather than hard coding the results. For example, the pandas shape method might be useful for calculating some of the summary results.

Provide a Basic Summary of the Data Set Using Python, Numpy and/or Pandas

A) Size & shape analysis of given dataset and generation for validation-set

The validation-set is splitted from the original trainings-set by 20%.

In [2]:
### Replace each question mark with the appropriate value. 
### Use python, pandas or numpy methods rather than hard coding the results

import numpy as np
from sklearn.model_selection import train_test_split

# Get randomized datasets for training and validation
X_train, X_validation, y_train, y_validation = train_test_split(X_train, y_train,test_size=0.2,random_state=0)

# Number of training examples
n_train = len(X_train)

# Number of validation examples
n_validation = len(X_validation)

# Number of testing examples.
n_test = len(X_test)

# What's the shape of an traffic sign image?
image_shape = X_train[0].shape

# How many unique classes/labels there are in the dataset.
n_classes = len(np.unique(y_train))

# print
print()
print("Number of training examples   =", n_train)
print("number of validation examples =", n_validation)
print("Number of testing examples    =", n_test)
print("Image data shape              =", image_shape)
print("Number of classes             =", n_classes)
Number of training examples   = 31367
number of validation examples = 7842
Number of testing examples    = 12630
Image data shape              = (32, 32, 3)
Number of classes             = 43

Visualize the German Traffic Signs Dataset using the pickled file(s). This is open ended, suggestions include: plotting traffic sign images, plotting the count of each sign, etc.

The Matplotlib examples and gallery pages are a great resource for doing visualizations in Python.

NOTE: It's recommended you start with something simple first. If you wish to do more, come back to it after you've completed the rest of the sections. It can be interesting to look at the distribution of classes in the training, validation and test set. Is the distribution the same? Are there more examples of some classes than others?

B) Analysis of the distribution of the training-set, validation-set and test-set

In [3]:
import matplotlib.pyplot as plt
%matplotlib inline

print ()
print ("Data distributions of training, validation and test sets")
print ()

# Show distribrution of training data
data, number = np.unique(y_train,return_counts=True)
plt.rcParams["figure.figsize"] = [10, 3]
axes = plt.gca()
axes.set_xlim([-1,n_classes])
plt.bar(data, number, tick_label=data, width=0.8, align='center')
plt.title('distribution of training data')
plt.show()

# Show distribution of validation data
data, number = np.unique(y_validation,return_counts=True)
plt.rcParams["figure.figsize"] = [10, 3]
axes = plt.gca()
axes.set_xlim([-1,n_classes])
plt.bar(data, number, tick_label=data, width=0.8, align='center')
plt.title('distribution of validation data')
plt.show()

# Show distribution of test data
data, number = np.unique(y_test,return_counts=True)
plt.rcParams["figure.figsize"] = [10, 3]
axes = plt.gca()
axes.set_xlim([-1,n_classes])
plt.bar(data, number, tick_label=data, width=0.8, align='center')
plt.title('distribution of test data')
plt.show()
Data distributions of training, validation and test sets

C) Visualization of training-set images

In [4]:
import random
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline


# Print 4 random pics of training data
print ()
print ('Display 4 random training pics examples (title represents the class id)')

# Save random numbers & pics for further steps
pic = np.zeros(4,dtype=int)
plt.figure(figsize=(10, 7))

# Plot images
for i in range(4) :
    pic[i] = random.randint(0, len(X_train))
    image = X_train[pic[i]]
    plt.subplot(4,4,i+1)
    plt.imshow(image)
    plt.title(y_train[pic[i]])
    
plt.tight_layout()
plt.show()

# Save pic random numbers
pic_save = np.array(pic,dtype=int)
Display 4 random training pics examples (title represents the class id)

Step 2: Design and Test a Model Architecture

Design and implement a deep learning model that learns to recognize traffic signs. Train and test your model on the German Traffic Sign Dataset.

The LeNet-5 implementation shown in the classroom at the end of the CNN lesson is a solid starting point. You'll have to change the number of classes and possibly the preprocessing, but aside from that it's plug and play!

With the LeNet-5 solution from the lecture, you should expect a validation set accuracy of about 0.89. To meet specifications, the validation set accuracy will need to be at least 0.93. It is possible to get an even higher accuracy, but 0.93 is the minimum for a successful project submission.

There are various aspects to consider when thinking about this problem:

  • Neural network architecture (is the network over or underfitting?)
  • Play around preprocessing techniques (normalization, rgb to grayscale, etc)
  • Number of examples per label (some have more than others).
  • Generate fake data.

Here is an example of a published baseline model on this problem. It's not required to be familiar with the approach used in the paper but, it's good practice to try to read papers like these.

Pre-process the Data Set (normalization, grayscale, etc.)

Minimally, the image data should be normalized so that the data has mean zero and equal variance. For image data, (pixel - 128)/ 128 is a quick way to approximately normalize the data and can be used in this project.

Other pre-processing steps are optional. You can try different techniques to see if it improves performance.

Use the code cell (or multiple code cells, if necessary) to implement the first step of your project.

A) Preprossing functions: normalize_input, processRGBpics

  • func. normalize_input, normalizes input image data by pixel - 128)/ 128 => [-1,+1] normalization
  • func. processRGBpics, converts color images to grayscale ones and performs a image/pixel normalization (see above)
In [5]:
# Normalize function    
def normalize_input(image) :
    return (image-128.0)/128

# ProcessRGBpics function coverts to grayscale and normalizes the image (-1,1) style by (pixel - 128)/ 128 
def processRGBpics(x):
    output = np.average(x, axis=3)
    output = np.expand_dims(output, axis=3)
    output =  normalize_input(output)
    return output

B) Pre-processing of training-set, validation-set and test-set data

In [6]:
# Process training, validation and test data (grayscale + normalization)
x_training   = processRGBpics(X_train)
x_validation = processRGBpics(X_validation)
x_test       = processRGBpics(X_test)

C) Visualization of pre-processed training-set images

In [7]:
# Plot the processed pics
print ()
print ('Display the 4 random training pics examples from before - processed (title represents the class id)')

plt.figure(figsize=(10, 7))

# Plot processed images
for i in range(len(pic_save)) :
    image = np.reshape(x_training[pic_save[i]],(32,32))
    plt.subplot(4,4,i+1)
    plt.imshow(image,cmap = plt.get_cmap('gray'))
    plt.title(y_train[pic_save[i]])

plt.tight_layout()
plt.show()   
Display the 4 random training pics examples from before - processed (title represents the class id)

D) Augmentation - Fake Image Generation

In order to increase the training-set random images are generated fromm the original training set. The following random actions are performed on the orignal images in order to create a "fake/new" image:

  1. width & height movements by +/-10%
  2. rotation +/-15 degrees
  3. zoom out/in by 90%/110%

The ImageDataGenerator from the keras enviroment is used to create the fake images.

In [8]:
import keras
from keras.preprocessing.image import ImageDataGenerator

# Setup image manipultion generator from keras
datagen = ImageDataGenerator(rotation_range = 30, width_shift_range = 0.10, height_shift_range = 0.10, zoom_range = 0.2)

# Size of fake data
BATCH     = 1000                    # batch size generator
RUN       = 3                       # number of runs
EXIT      = int(len(y_train)/BATCH) # exit loops criteria

x_fake =np.zeros((BATCH*EXIT*RUN,32,32,3))
y_fake =np.zeros(BATCH*EXIT*RUN,dtype=np.int)

print ("(debug) Fake images generation started")

for u in range (RUN):
    i = 0
    k = u * EXIT * BATCH
    print ('(debug) loop',u,'of',BATCH*EXIT,'runs ...')
    datagen = ImageDataGenerator(rotation_range = 30, width_shift_range = 0.10, height_shift_range = 0.10, zoom_range = 0.2)
    for x_, y_ in datagen.flow(X_train, y_train, batch_size = BATCH):
        start = k + i * BATCH
        end   = start + BATCH 
        y_fake[start:end] = np.copy(y_)
        x_fake[start:end] = np.copy(x_) 

        i = i + 1
        if (i>EXIT-1) :
            break
    
                   
print ("(debug) Fake images generated !")
Using TensorFlow backend.
(debug) Fake images generation started
(debug) loop 0 of 31000 runs ...
(debug) loop 1 of 31000 runs ...
(debug) loop 2 of 31000 runs ...
(debug) Fake images generated !

D1) Visualization of generated fake images

In [9]:
# Plot 4 random fake images
print ()
print ('Display the 4 random fake pics examples (title represents the class id)')


# Save random numbers & pics for further steps
pic_f = np.zeros(4,dtype=int)
plt.figure(figsize=(10, 7))

# Plot images
for i in range(4) :
    pic_f[i] = random.randint(0, len(x_fake))
    image = x_fake[pic_f[i]]
    plt.subplot(4,4,i+1)
    plt.imshow(image)
    plt.title(y_fake[pic_f[i]])
    
plt.tight_layout()
plt.show()

# Save pic random numbers
pic_f_save = np.array(pic_f,dtype=int)
Display the 4 random fake pics examples (title represents the class id)

D2) Pre-process augmentated images

In [10]:
# Process fake image data (grayscale + normalization)

x_training_f   = processRGBpics(x_fake)

D3) Visualization of pre-processed augmentated images

In [11]:
# Plot the processed pics
print ()
print ('Display the 4 random fake pics examples from before - processed (title represents the class id)')

plt.figure(figsize=(10, 7))

# Plot processed images
for i in range(len(pic_f_save)) :
    image = np.reshape(x_training_f[pic_f_save[i]],(32,32))
    plt.subplot(4,4,i+1)
    plt.imshow(image,cmap = plt.get_cmap('gray'))
    plt.title(y_fake[pic_f_save[i]])

plt.tight_layout()
plt.show()   
Display the 4 random fake pics examples from before - processed (title represents the class id)

D5) Add the augmentated images to the training-set

In [12]:
# Join fake data to training data (training data = orginal data + fake data)
print ()
print ('(debug) Adding fake data with size =',len(y_fake),'to training data with size=',len(y_train))
print ('(debug) New training set size =',(len(y_fake)+len(y_train))  )
print ()

x_training = np.concatenate((x_training, x_training_f))
y_train    = np.concatenate((y_train, y_fake))
(debug) Adding fake data with size = 93000 to training data with size= 31367
(debug) New training set size = 124367

D6) Analysis of the distribution of the training-set after adding augmentated images

In [13]:
# Display distribution of new data
print ()
print ("Data distributions of training after joining in fake data")
print ()

data, number = np.unique(y_train,return_counts=True)
plt.rcParams["figure.figsize"] = [10, 3]
axes = plt.gca()
axes.set_xlim([-1,n_classes])
plt.bar(data, number, tick_label=data, width=0.8, align='center')
plt.title('distribution of training data = orginal + augmentated')
plt.show()
Data distributions of training after joining in fake data

D7) Model Architecture based on LeNet architecture

In [57]:
from sklearn.utils import shuffle
# shuffle training data
x_training, y_train = shuffle(x_training, y_train)

D7.1) Model functions & Parameters

In [54]:
### Define your architecture here.
### Feel free to use as many code cells as needed.
import tensorflow as tf
from tensorflow.contrib.layers import flatten


# training parameters global
EPOCHS = 30
BATCH_SIZE = 128

# Random variables
mu = 0
sigma = 0.1


def conv1_activation(x) :

    #SOLUTION: Layer 1: Convolutional. Input = 32x32x1. Output = 30x30x15.
    conv1_W = tf.Variable(tf.truncated_normal(shape=(3, 3, 1, 15), mean = mu, stddev = sigma), name = 'conv1_W')
    conv1_b = tf.Variable(tf.zeros(15), name = 'conv1_b')
    conv1   = tf.nn.conv2d(x, conv1_W, strides=[1, 1, 1, 1], padding='VALID') + conv1_b    
    # SOLUTION: Activation.
    conv1 = tf.nn.relu(conv1)    
    return conv1


def conv1_subsample(x) :

    # SOLUTION: Pooling. Input = 30x30x15. Output = 15x15x15
    conv1 = tf.nn.max_pool(x, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='VALID')
    return conv1


def conv2_activation(x):

    # SOLUTION: Layer 2: Convolutional. Output = 11x11x16.
    conv2_W = tf.Variable(tf.truncated_normal(shape=(5, 5, 15, 16), mean = mu, stddev = sigma),name = 'conv2_W')
    conv2_b = tf.Variable(tf.zeros(16), name = 'conv2_b')
    conv2   = tf.nn.conv2d(x, conv2_W, strides=[1, 1, 1, 1], padding='VALID') + conv2_b
    # SOLUTION: Activation.
    conv2 = tf.nn.relu(conv2)
    return conv2


def conv2_subsample(x):
    
    # SOLUTION: Pooling. Input = 11x11x16. Output = 5x5x16.
    conv2 = tf.nn.max_pool(x, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='VALID')
    return conv2


def fc0_conv1_subsample(x):

    # SOLUTION: Pooling. Input = 15x15x15. Output = 7x7x15
    sub1   = tf.nn.max_pool(x, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='VALID') 
    return sub1


def fc0_net(x,y):

    fc0   = tf.concat([flatten(y),flatten(x)],1)
    return fc0


def fc1_net(x):

    # SOLUTION: Layer 3: Fully Connected. Input = 400 + 735 = 1135. Output = 120.
    fc1_W = tf.Variable(tf.truncated_normal(shape=(1135, 120), mean = mu, stddev = sigma), name = 'fc1_W')#
    fc1_b = tf.Variable(tf.zeros(120), name = 'fc1_b')
    fc1   = tf.matmul(x, fc1_W) + fc1_b
    
    # SOLUTION: Activation.
    fc1    = tf.nn.relu(fc1)
    return fc1


def fc2_net(x):

    # SOLUTION: Layer 4: Fully Connected. Input = 120. Output = 84.
    fc2_W  = tf.Variable(tf.truncated_normal(shape=(120, 84), mean = mu, stddev = sigma), name = 'fc2_W')
    fc2_b  = tf.Variable(tf.zeros(84), name = 'fc2_b')
    fc2    = tf.matmul(x, fc2_W) + fc2_b
    
    # SOLUTION: Activation.
    fc2    = tf.nn.relu(fc2)
    return fc2


def LeNet(x):    

    # SOLUTION: Layer 5: Fully Connected. Input = 84. Output = 43.
    fc3_W  = tf.Variable(tf.truncated_normal(shape=(84, 43), mean = mu, stddev = sigma), name = 'fc3_W')
    fc3_b  = tf.Variable(tf.zeros(43), name = 'fc3_b')
    logits = tf.matmul(x, fc3_W) + fc3_b    
    return logits

A validation set can be used to assess how well the model is performing. A low accuracy on the training and validation sets imply underfitting. A high accuracy on the training set but low accuracy on the validation set implies overfitting.

D8) Model Intantiation

In [58]:
### Train your model here.
### Calculate and report the accuracy on the training and validation set.
### Once a final model architecture is selected, 
### the accuracy on the test set should be calculated and reported as well.
### Feel free to use as many code cells as needed.

x = tf.placeholder(tf.float32, (None, 32, 32, 1))  # color map
y = tf.placeholder(tf.int32, (None))
one_hot_y = tf.one_hot(y, 43)                      # 43 feautres


rate = 0.001

# model network
conv1_act       = conv1_activation(x)
conv1_sub       = conv1_subsample(conv1_act)
conv2_act       = conv2_activation(conv1_sub)
conv2_sub       = conv2_subsample(conv2_act)
fc0_conv1_sub   = fc0_conv1_subsample(conv1_sub)
fc0             = fc0_net(fc0_conv1_sub,conv2_sub) 
fc1             = fc1_net(fc0)
fc2             = fc2_net(fc1)
logits          = LeNet(fc2)


cross_entropy = tf.nn.softmax_cross_entropy_with_logits(labels=one_hot_y, logits=logits)
loss_operation = tf.reduce_mean(cross_entropy)
optimizer = tf.train.AdamOptimizer(learning_rate = rate)
training_operation = optimizer.minimize(loss_operation)


# evaluate
correct_prediction = tf.equal(tf.argmax(logits, 1), tf.argmax(one_hot_y, 1))
accuracy_operation = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))

D9) Mode Saver & Evaluation function

In [59]:
saver = tf.train.Saver()

def evaluate(X_data, y_data):
    num_examples = len(X_data)
    total_accuracy = 0
    sess = tf.get_default_session() 
    for offset in range(0, num_examples, BATCH_SIZE):
        batch_x, batch_y = X_data[offset:offset+BATCH_SIZE], y_data[offset:offset+BATCH_SIZE]
        accuracy = sess.run(accuracy_operation, feed_dict={x: batch_x, y: batch_y})
        total_accuracy += (accuracy * len(batch_x))
    return total_accuracy / num_examples

D10) Train model

In [61]:
from datetime import datetime

# run session
with tf.Session() as sess:
    sess.run(tf.global_variables_initializer())
    num_examples = len(x_training) # independent 
    v_accuracy = np.zeros(EPOCHS)
    v_training = np.zeros(EPOCHS)
    
    print("Training...")
    print()
    
    start_time = datetime.now() # start timer 
    
    for i in range(EPOCHS):
        x_training, y_train = shuffle(x_training, y_train)
                
        for offset in range(0, num_examples, BATCH_SIZE):
            end = offset + BATCH_SIZE
            batch_x, batch_y = x_training[offset:end], y_train[offset:end]
            sess.run(training_operation, feed_dict={x: batch_x, y: batch_y})
        
        training_accuracy = evaluate (x_training, y_train)
        v_training[i] = training_accuracy
        
        validation_accuracy = evaluate(x_validation, y_validation)
        v_accuracy[i] = validation_accuracy    
                
        print("EPOCH {} ...".format(i+1))
        print("Training   Accuracy = {:.3f}".format(training_accuracy))   # this perf. inefficient
        print("Validation Accuracy = {:.3f}".format(validation_accuracy))
        print()
      
        v_test = evaluate (x_test, y_test)
        
    print("Test       Accuracy = {:.3f}".format(v_test))
    
    saver.save(sess, './org-custom-lenet')
    
    print("Run time =",datetime.now()-start_time) # performance
    print()
    
    print("Model saved")
Training...

EPOCH 1 ...
Training   Accuracy = 0.742
Validation Accuracy = 0.742

EPOCH 2 ...
Training   Accuracy = 0.906
Validation Accuracy = 0.893

EPOCH 3 ...
Training   Accuracy = 0.924
Validation Accuracy = 0.915

EPOCH 4 ...
Training   Accuracy = 0.958
Validation Accuracy = 0.944

EPOCH 5 ...
Training   Accuracy = 0.970
Validation Accuracy = 0.957

EPOCH 6 ...
Training   Accuracy = 0.974
Validation Accuracy = 0.960

EPOCH 7 ...
Training   Accuracy = 0.983
Validation Accuracy = 0.969

EPOCH 8 ...
Training   Accuracy = 0.989
Validation Accuracy = 0.977

EPOCH 9 ...
Training   Accuracy = 0.989
Validation Accuracy = 0.973

EPOCH 10 ...
Training   Accuracy = 0.988
Validation Accuracy = 0.974

EPOCH 11 ...
Training   Accuracy = 0.994
Validation Accuracy = 0.979

EPOCH 12 ...
Training   Accuracy = 0.991
Validation Accuracy = 0.975

EPOCH 13 ...
Training   Accuracy = 0.988
Validation Accuracy = 0.974

EPOCH 14 ...
Training   Accuracy = 0.995
Validation Accuracy = 0.981

EPOCH 15 ...
Training   Accuracy = 0.996
Validation Accuracy = 0.981

EPOCH 16 ...
Training   Accuracy = 0.996
Validation Accuracy = 0.982

EPOCH 17 ...
Training   Accuracy = 0.990
Validation Accuracy = 0.973

EPOCH 18 ...
Training   Accuracy = 0.991
Validation Accuracy = 0.972

EPOCH 19 ...
Training   Accuracy = 0.998
Validation Accuracy = 0.983

EPOCH 20 ...
Training   Accuracy = 0.992
Validation Accuracy = 0.978

EPOCH 21 ...
Training   Accuracy = 0.995
Validation Accuracy = 0.981

EPOCH 22 ...
Training   Accuracy = 0.992
Validation Accuracy = 0.977

EPOCH 23 ...
Training   Accuracy = 0.995
Validation Accuracy = 0.982

EPOCH 24 ...
Training   Accuracy = 0.999
Validation Accuracy = 0.988

EPOCH 25 ...
Training   Accuracy = 0.999
Validation Accuracy = 0.988

EPOCH 26 ...
Training   Accuracy = 0.999
Validation Accuracy = 0.986

EPOCH 27 ...
Training   Accuracy = 0.997
Validation Accuracy = 0.984

EPOCH 28 ...
Training   Accuracy = 0.996
Validation Accuracy = 0.982

EPOCH 29 ...
Training   Accuracy = 0.998
Validation Accuracy = 0.986

EPOCH 30 ...
Training   Accuracy = 0.999
Validation Accuracy = 0.987

Test       Accuracy = 0.932
Run time = 0:26:22.613106

Model saved

Train, Validate and Test the Model

Include an exploratory visualization of the dataset

A) Visualization of trainings and validation accuracy vs. number of epchos

In [62]:
# Plot accuracies
plt.figure(figsize=(26, 10))
plt.subplot(221)
plt.title('Training & Validation Accuracy',fontsize=14)
    
plt.plot(range(1,EPOCHS+1),(v_accuracy),  'ro')
plt.plot(range(1,EPOCHS+1),(v_accuracy),  'r-')
plt.plot(range(1,EPOCHS+1),(v_training),  'b--')
    
#plt.text(1,3,'Validation Accuracy',color='red',fontsize=8)
    
plt.axis([1, EPOCHS, 0, 1.0])
plt.xticks(np.arange(1, EPOCHS+1, 1))
plt.grid()
plt.xlabel('Epochs',fontsize=12)
plt.ylabel('Accuracy',fontsize=12)
plt.text(2,0.2,'Training Accuracy',color='blue',fontsize=12)
plt.text(2,0.15,'Validation Accuracy',color='red',fontsize=12)
plt.text(2,0.1,'Test Accuracy',color='black',fontsize=12)
plt.text(6,0.1,v_test,color='black',fontsize=12)
plt.tight_layout()
plt.show()

Step 3: Test a Model on New Images

To give yourself more insight into how your model is working, download at least five pictures of German traffic signs from the web and use your model to predict the traffic sign type.

You may find signnames.csv useful as it contains mappings from the class id (integer) to the actual sign name.

Load and Output the Images

A) Helper array to map class label type ID to traffic signs names

In [63]:
### Traffic Sign Nsme Mapping function
import numpy as np

with open('signnames.csv') as f:
    content = f.read().splitlines()

sign_names = []
# Map to array
for i in range (1,len(content),1) :
    name = content[i].split(',')
    sign_names.append(name[1])

B) Load 16 traffic sign images from the WWW and visualize these

The file name of each image codes the traffic sign class label inside the name: sign_[name].[class type ID].jpg

In [64]:
### Load the images and plot them here.
### Feel free to use as many code cells as needed.
import os
import matplotlib.pyplot as plt
import matplotlib.image as mpimg

# pics location
location = "test_examples.new.good/"
test_dir = os.listdir(location)

pic_features  = []
pic_labels    = []


# plot pics
plt.figure(figsize=(10, 7))

i = 0
for pics in test_dir :
    # read image file
    fname  = location + pics
    image = mpimg.imread(fname)
    name  = pics.split('.')
    # build list   
    pic_features.append(image)
    pic_labels.append(name[1])
    # print 
    #print (pics.shape)
    #print (pics)
    # show
    sub = plt.subplot(5,4,i+1)
    plt.imshow(image)
    plt.title(pics)
    i = i + 1
    
        
print ()
print ("(debug)",i, "traffic sign images from the WWW")
print ()
    
    
plt.tight_layout()
plt.show()

# pre-process pics for prediction
newfeatures   = processRGBpics(np.array(pic_features))
newlabels     = np.array(pic_labels,dtype=np.int)
(debug) 16 traffic sign images from the WWW

Predict the Sign Type for Each Image

C) Predict & visualize sign type prediction for each image (including indirect performance analysis)

In [65]:
### Run the predictions here and use the model to output the prediction for each image.
### Make sure to pre-process the images with the same pre-processing pipeline used earlier.
### Feel free to use as many code cells as needed.

import tensorflow as tf

print ()
print ('Sample pics prediction (right pic = original, left pic = used inside model')
print ()

# Run prediction on each pic
i = 0
for pics in newfeatures :
    # run predcition with trained model
    with tf.Session() as sess:
        saver.restore(sess, tf.train.latest_checkpoint('.'))    
        image = np.reshape(pics,(1,32,32,1))
        prediction = sess.run(tf.argmax(logits,1), feed_dict={x: image})
        # print prediction 
        print ('  SIGN:',sign_names[newlabels[i]] ,' PREDICTION:',sign_names[prediction[0]])
        # plot images
        plt.subplot(2,2,1)
        plt.imshow(pic_features[i])
        i = i + 1
        plt.subplot(2,2,2)
        ima= np.reshape(pics,(32,32))
        plt.imshow(ima,cmap = plt.get_cmap('gray'))
        plt.tight_layout()
        plt.show()
Sample pics prediction (right pic = original, left pic = used inside model

  SIGN: Speed limit (30km/h)  PREDICTION: Speed limit (70km/h)
  SIGN: No entry  PREDICTION: No entry
  SIGN: Turn right ahead  PREDICTION: Turn right ahead
  SIGN: Right-of-way at the next intersection  PREDICTION: Right-of-way at the next intersection
  SIGN: Go straight or left  PREDICTION: Go straight or left
  SIGN: Slippery road  PREDICTION: Slippery road
  SIGN: Slippery road  PREDICTION: Speed limit (20km/h)
  SIGN: Priority road  PREDICTION: Priority road
  SIGN: No vehicles  PREDICTION: No passing
  SIGN: No vehicles  PREDICTION: No vehicles
  SIGN: Speed limit (30km/h)  PREDICTION: Speed limit (30km/h)
  SIGN: Speed limit (120km/h)  PREDICTION: Speed limit (120km/h)
  SIGN: Speed limit (80km/h)  PREDICTION: Slippery road
  SIGN: General caution  PREDICTION: Right-of-way at the next intersection
  SIGN: Children crossing  PREDICTION: Turn left ahead
  SIGN: General caution  PREDICTION: Road narrows on the right

Analyze Performance

D) Performance analysis about the 16 traffic sign images from the WWW vs. model (prediction vs. real)

In [66]:
# run prediction and accuracy on 16 traffic sign images from the WWW
with tf.Session() as sess:
    saver.restore(sess, tf.train.latest_checkpoint('.'))
    # test accuracy on test-set
    test_accuracy = evaluate(x_test, y_test)
    print("(debug) Test accuracy = {:.3f}".format(test_accuracy))
    # accuracy analysis on 16 traffic signs from WWW
    test_accuracy = evaluate(newfeatures, newlabels)
    print("(debug) New pics accuracy = {:.3f}".format(test_accuracy))
    print("(debug) Prediction perf. analysis for new pics".format(test_accuracy))
    # performance analysis on 16 traffic signs from WWW
    hits       = sess.run(correct_prediction,  feed_dict={x: newfeatures, y: newlabels})
    prediction = sess.run(tf.argmax(logits,1), feed_dict={x: newfeatures})

# visualize accuracy & performance
# plot analysis vs. images
plt.figure(figsize=(26, 10))
plt.subplot(221)
plt.title('Predicted vs. Real Class Id of Sample Pics')
plt.plot(range(len(newlabels)), newlabels,  'bo')
plt.plot(range(len(newlabels)), newlabels,  'b--')
plt.text(1,5,'Real Class Id',color='blue',fontsize=8)
plt.text(1,3,'Predicted Class Id',color='red',fontsize=8)
plt.plot(range(len(newlabels)), prediction, 'r--')
plt.axis([0, len(newlabels)-1, 0, 43])
plt.xticks(np.arange(0, len(newlabels), 1))
plt.grid()
plt.xlabel('Pic Sample')
plt.ylabel('Class Id')
plt.show()

# plot images
i = 0
plt.figure(figsize=(24, 6))
for pics in pic_features :
    if (i>=len(newlabels)) :
        break
    sub = plt.subplot(4,16,i+1)
    sub.set_xticks(())
    sub.set_yticks(())
    plt.imshow(pics)
    plt.title(hits[i],fontsize=30)
    i = i + 1  
plt.tight_layout()
plt.show()
    
(debug) Test accuracy = 0.932
(debug) New pics accuracy = 0.562
(debug) Prediction perf. analysis for new pics
In [34]:
### Calculate the accuracy for these 5 new images. 
### For example, if the model predicted 1 out of 5 signs correctly, it's 20% accurate on these new images.

Output Top 5 Softmax Probabilities For Each Image Found on the Web

For each of the new images, print out the model's softmax probabilities to show the certainty of the model's predictions (limit the output to the top 5 probabilities for each image). tf.nn.top_k could prove helpful here.

The example below demonstrates how tf.nn.top_k can be used to find the top k predictions for each image.

tf.nn.top_k will return the values and indices (class ids) of the top k predictions. So if k=3, for each sign, it'll return the 3 largest probabilities (out of a possible 43) and the correspoding class ids.

Take this numpy array as an example. The values in the array represent predictions. The array contains softmax probabilities for five candidate images with six possible classes. tk.nn.top_k is used to choose the three classes with the highest probability:

# (5, 6) array
a = np.array([[ 0.24879643,  0.07032244,  0.12641572,  0.34763842,  0.07893497,
         0.12789202],
       [ 0.28086119,  0.27569815,  0.08594638,  0.0178669 ,  0.18063401,
         0.15899337],
       [ 0.26076848,  0.23664738,  0.08020603,  0.07001922,  0.1134371 ,
         0.23892179],
       [ 0.11943333,  0.29198961,  0.02605103,  0.26234032,  0.1351348 ,
         0.16505091],
       [ 0.09561176,  0.34396535,  0.0643941 ,  0.16240774,  0.24206137,
         0.09155967]])

Running it through sess.run(tf.nn.top_k(tf.constant(a), k=3)) produces:

TopKV2(values=array([[ 0.34763842,  0.24879643,  0.12789202],
       [ 0.28086119,  0.27569815,  0.18063401],
       [ 0.26076848,  0.23892179,  0.23664738],
       [ 0.29198961,  0.26234032,  0.16505091],
       [ 0.34396535,  0.24206137,  0.16240774]]), indices=array([[3, 0, 5],
       [0, 1, 4],
       [0, 5, 1],
       [1, 3, 5],
       [1, 4, 3]], dtype=int32))

Looking just at the first row we get [ 0.34763842, 0.24879643, 0.12789202], you can confirm these are the 3 largest probabilities in a. You'll also notice [3, 0, 5] are the corresponding indices.

E) Analsis of top 5 probabilities of 16 traffic signs images from the WWW

In [67]:
### Print out the top five softmax probabilities for the predictions on the German traffic sign images found on the web. 
### Feel free to use as many code cells as needed.

# run softmax func. on trained model for the 16 traffic signs from the WWW
with tf.Session() as sess:
    saver.restore(sess, tf.train.latest_checkpoint('.'))
    soft_top5 = sess.run(tf.nn.top_k(tf.nn.softmax(logits),k=5), feed_dict={x: newfeatures})
    print ('Top 5 probabilities of the sample pics (first = original, second used @ model)')
    print ()
   
# Plot analysis 
#sign_  = np.array(['123456789123456789123456789','123456789123456789123456789','123456789123456789123456789','123456789123456789123456789','123456789123456789123456789'])
#prop_  = np.array([0.0, 0.0, 0.0, 0.0, 0.0])


i = 0
for pics in pic_features :
    if (i>=len(newlabels)) :
        break
    
    print ('SIGN:',sign_names[newlabels[i]],' RECOGNIZED:',hits[i])
    
    for u in range(5) :
        print ('      top (',u+1,') {:.20f}'.format(soft_top5[0][i][u]), sign_names[soft_top5[1][i][u]])
        #prop_[u]  = soft_top5[0][i][u]
        #sign_[u]  = sign_names[soft_top5[1][i][u]]

    plt.subplot(2,2,1)
    plt.imshow(pics)
    plt.subplot(2,2,2)
    ima= np.reshape(newfeatures[i],(32,32))
    plt.imshow(ima,cmap = plt.get_cmap('gray'))

    #plt.subplot(2,2,3)
    #y_pos = np.arange(len(sign_))
    #plt.title('Top 5 Probabilities')
    #plt.barh(y_pos,prop_,align='center',)
    #plt.yticks(y_pos, sign_)
    #plt.xticks(prop_)
    #plt.xlabel(x_pos)
    #plt.xticks(())
    #plt.yticks(())
    #plt.xlabel('Probability')
                               
                        
    plt.tight_layout()
    plt.show()
    i = i + 1
     
Top 5 probabilities of the sample pics (first = original, second used @ model)

SIGN: Speed limit (30km/h)  RECOGNIZED: False
      top ( 1 ) 0.99992144107818603516 Speed limit (70km/h)
      top ( 2 ) 0.00007696625107200816 Speed limit (50km/h)
      top ( 3 ) 0.00000083608955492309 Stop
      top ( 4 ) 0.00000068090128024778 Speed limit (30km/h)
      top ( 5 ) 0.00000000001560197631 Speed limit (20km/h)
SIGN: No entry  RECOGNIZED: True
      top ( 1 ) 1.00000000000000000000 No entry
      top ( 2 ) 0.00000000000000004185 Turn right ahead
      top ( 3 ) 0.00000000000000003356 Turn left ahead
      top ( 4 ) 0.00000000000000000980 Stop
      top ( 5 ) 0.00000000000000000175 Bicycles crossing
SIGN: Turn right ahead  RECOGNIZED: True
      top ( 1 ) 0.99999988079071044922 Turn right ahead
      top ( 2 ) 0.00000014091803279825 Ahead only
      top ( 3 ) 0.00000001456393050603 Road narrows on the right
      top ( 4 ) 0.00000000000232284352 Road work
      top ( 5 ) 0.00000000000195126792 Go straight or left
SIGN: Right-of-way at the next intersection  RECOGNIZED: True
      top ( 1 ) 1.00000000000000000000 Right-of-way at the next intersection
      top ( 2 ) 0.00000000000000000460 Double curve
      top ( 3 ) 0.00000000000000000141 Beware of ice/snow
      top ( 4 ) 0.00000000000000000000 Road work
      top ( 5 ) 0.00000000000000000000 General caution
SIGN: Go straight or left  RECOGNIZED: True
      top ( 1 ) 1.00000000000000000000 Go straight or left
      top ( 2 ) 0.00000000048975368205 No entry
      top ( 3 ) 0.00000000002292173049 Roundabout mandatory
      top ( 4 ) 0.00000000000041002737 Slippery road
      top ( 5 ) 0.00000000000020682182 Keep left
SIGN: Slippery road  RECOGNIZED: True
      top ( 1 ) 1.00000000000000000000 Slippery road
      top ( 2 ) 0.00000000000000000049 Beware of ice/snow
      top ( 3 ) 0.00000000000000000000 Double curve
      top ( 4 ) 0.00000000000000000000 Right-of-way at the next intersection
      top ( 5 ) 0.00000000000000000000 Dangerous curve to the right
SIGN: Slippery road  RECOGNIZED: False
      top ( 1 ) 0.99993944168090820312 Speed limit (20km/h)
      top ( 2 ) 0.00005689891622751020 Roundabout mandatory
      top ( 3 ) 0.00000371043438462948 Speed limit (30km/h)
      top ( 4 ) 0.00000000165818181408 Traffic signals
      top ( 5 ) 0.00000000052337578715 Wild animals crossing
SIGN: Priority road  RECOGNIZED: True
      top ( 1 ) 1.00000000000000000000 Priority road
      top ( 2 ) 0.00000000000000001417 Roundabout mandatory
      top ( 3 ) 0.00000000000000000000 End of no passing
      top ( 4 ) 0.00000000000000000000 End of all speed and passing limits
      top ( 5 ) 0.00000000000000000000 Yield
SIGN: No vehicles  RECOGNIZED: False
      top ( 1 ) 0.99864798784255981445 No passing
      top ( 2 ) 0.00134783785324543715 Slippery road
      top ( 3 ) 0.00000398034626414301 Yield
      top ( 4 ) 0.00000024362469730477 Speed limit (50km/h)
      top ( 5 ) 0.00000000007727519319 End of no passing
SIGN: No vehicles  RECOGNIZED: True
      top ( 1 ) 0.99890208244323730469 No vehicles
      top ( 2 ) 0.00109593570232391357 Priority road
      top ( 3 ) 0.00000194103313333471 Roundabout mandatory
      top ( 4 ) 0.00000005951898884859 End of all speed and passing limits
      top ( 5 ) 0.00000000000532002524 No passing
SIGN: Speed limit (30km/h)  RECOGNIZED: True
      top ( 1 ) 1.00000000000000000000 Speed limit (30km/h)
      top ( 2 ) 0.00000000000000510715 Speed limit (20km/h)
      top ( 3 ) 0.00000000000000000000 Speed limit (80km/h)
      top ( 4 ) 0.00000000000000000000 Speed limit (50km/h)
      top ( 5 ) 0.00000000000000000000 Roundabout mandatory
SIGN: Speed limit (120km/h)  RECOGNIZED: True
      top ( 1 ) 0.97832608222961425781 Speed limit (120km/h)
      top ( 2 ) 0.01821508444845676422 Speed limit (50km/h)
      top ( 3 ) 0.00221867254003882408 Speed limit (30km/h)
      top ( 4 ) 0.00116828677710145712 Speed limit (80km/h)
      top ( 5 ) 0.00007172222103690729 Speed limit (20km/h)
SIGN: Speed limit (80km/h)  RECOGNIZED: False
      top ( 1 ) 0.99982637166976928711 Slippery road
      top ( 2 ) 0.00016014919674489647 Roundabout mandatory
      top ( 3 ) 0.00001339767368335743 Go straight or left
      top ( 4 ) 0.00000014848212970264 Dangerous curve to the right
      top ( 5 ) 0.00000000292749557929 Keep right
SIGN: General caution  RECOGNIZED: False
      top ( 1 ) 0.97561353445053100586 Right-of-way at the next intersection
      top ( 2 ) 0.02226061373949050903 General caution
      top ( 3 ) 0.00175580696668475866 Pedestrians
      top ( 4 ) 0.00035783444764092565 End of all speed and passing limits
      top ( 5 ) 0.00000950620142248226 Traffic signals
SIGN: Children crossing  RECOGNIZED: False
      top ( 1 ) 0.80761754512786865234 Turn left ahead
      top ( 2 ) 0.09815347194671630859 End of no passing
      top ( 3 ) 0.08341362327337265015 Slippery road
      top ( 4 ) 0.01062205899506807327 Speed limit (60km/h)
      top ( 5 ) 0.00018462585285305977 Bicycles crossing
SIGN: General caution  RECOGNIZED: False
      top ( 1 ) 0.99996972084045410156 Road narrows on the right
      top ( 2 ) 0.00002439473792037461 General caution
      top ( 3 ) 0.00000381597510568099 Road work
      top ( 4 ) 0.00000200285057871952 Pedestrians
      top ( 5 ) 0.00000001963087647994 Traffic signals

Project Writeup

Once you have completed the code implementation, document your results in a project writeup using this template as a guide. The writeup can be in a markdown or pdf file.

Note: Once you have completed all of the code implementations and successfully answered each question above, you may finalize your work by exporting the iPython Notebook as an HTML document. You can do this by using the menu above and navigating to \n", "File -> Download as -> HTML (.html). Include the finished document along with this notebook as your submission.


Step 4 (Optional): Visualize the Neural Network's State with Test Images

This Section is not required to complete but acts as an additional excersise for understaning the output of a neural network's weights. While neural networks can be a great learning device they are often referred to as a black box. We can understand what the weights of a neural network look like better by plotting their feature maps. After successfully training your neural network you can see what it's feature maps look like by plotting the output of the network's weight layers in response to a test stimuli image. From these plotted feature maps, it's possible to see what characteristics of an image the network finds interesting. For a sign, maybe the inner network feature maps react with high activation to the sign's boundary outline or to the contrast in the sign's painted symbol.

Provided for you below is the function code that allows you to get the visualization output of any tensorflow weight layer you want. The inputs to the function should be a stimuli image, one used during training or a new one you provided, and then the tensorflow variable name that represents the layer's state during the training process, for instance if you wanted to see what the LeNet lab's feature maps looked like for it's second convolutional layer you could enter conv2 as the tf_activation variable.

For an example of what feature map outputs look like, check out NVIDIA's results in their paper End-to-End Deep Learning for Self-Driving Cars in the section Visualization of internal CNN State. NVIDIA was able to show that their network's inner weights had high activations to road boundary lines by comparing feature maps from an image with a clear path to one without. Try experimenting with a similar test to show that your trained network's weights are looking for interesting features, whether it's looking at differences in feature maps from images with or without a sign, or even what feature maps look like in a trained network vs a completely untrained one on the same sign image.

Combined Image

Your output should look something like this (above)

In [68]:
## Visualize your network's feature maps here.
### Feel free to use as many code cells as needed.

# image_input: the test image being fed into the network to produce the feature maps
# tf_activation: should be a tf variable name used during your training procedure that represents the calculated state of a specific weight layer
# activation_min/max: can be used to view the activation contrast in more detail, by default matplot sets min and max to the actual min and max values of the output
# plt_num: used to plot out multiple different weight feature map sets on the same block, just extend the plt number for each new feature map entry

#
# WARNING: changed function outputFeatureMap in order to adapt model structure
# + removed: image_input
# + changed code line: <old> activation = tf_activation.eval(session=sess,feed_dict={x : image_input})
#                      <new> activation = tf_activation

#
# <old> def outputFeatureMap(image_input, tf_activation, activation_min=-1, activation_max=-1 ,plt_num=1):
#

def outputFeatureMap(tf_activation, activation_min=-1, activation_max=-1 ,plt_num=1):
    # Here make sure to preprocess your image_input in a way your network expects
    # with size, normalization, ect if needed
    # image_input =
    # Note: x should be the same name as your network's tensorflow data placeholder variable
    # If you get an error tf_activation is not defined it may be having trouble accessing the variable from inside a function
    # <old> activation = tf_activation.eval(session=sess,feed_dict={x : image_input})
    activation = tf_activation
    featuremaps = activation.shape[3]
    plt.figure(plt_num, figsize=(15,15))
    for featuremap in range(featuremaps):
        plt.subplot(6,8, featuremap+1) # sets the number of feature maps to show on each row and column
        plt.title('FeatureMap ' + str(featuremap)) # displays the feature map number
        if activation_min != -1 & activation_max != -1:
            plt.imshow(activation[0,:,:, featuremap], interpolation="nearest", vmin =activation_min, vmax=activation_max, cmap="gray")
        elif activation_max != -1:
            plt.imshow(activation[0,:,:, featuremap], interpolation="nearest", vmax=activation_max, cmap="gray")
        elif activation_min !=-1:
            plt.imshow(activation[0,:,:, featuremap], interpolation="nearest", vmin=activation_min, cmap="gray")
        else:
            plt.imshow(activation[0,:,:, featuremap], interpolation="nearest", cmap="gray")
            

A) Select input image for featuremap analysis, display

Variable [image_id] specifies one image of 16 traffic sign images from the WWW

In [69]:
# Image-Featuremaps to display 
image_id = 5

print ()
print ('Input image for featuremaps plot (left = original, right = used inside model')

# Visualize input image
plt.subplot(2,2,1)
plt.imshow(pic_features[image_id])
plt.subplot(2,2,2)
im = np.reshape(newfeatures[image_id],(32,32))
plt.imshow(im,cmap = plt.get_cmap('gray'))
plt.tight_layout()
plt.show()

# Prepare image for featuremap display
image = newfeatures[image_id]
image = np.reshape(image,(1,32,32,1))
Input image for featuremaps plot (left = original, right = used inside model

B) Display featuremaps of model

Following featuremaps are displayed:

  • conv1 with and without pooling
  • conv2 with and without pooling
  • 2nd pooling of conv1 which is feed fully connected network fc1 (multi-scale)
In [70]:
# Feature map visualization
with tf.Session() as sess:
    # restore session
    saver.restore(sess, tf.train.latest_checkpoint('.'))
    
    # Visualize featuremap of conv1
    print ()
    print ("Featuremap of conv1 network 30x30x15:")
    conv1_ = sess.run(conv1_act, feed_dict={x: image})
    outputFeatureMap(conv1_)
    plt.tight_layout()
    plt.show()
    
    print ()
    print ("Featuremap of conv1 network after pooling 15x15x15:")
    conv1_sub_ = sess.run(conv1_sub, feed_dict={x: image})
    outputFeatureMap(conv1_sub_)
    plt.tight_layout()
    plt.show()
    
    # Visualize featuremap of conv2
    print ()
    print ("Featuremap of conv2 network 11x11x16:")
    conv2_ = sess.run(conv2_act, feed_dict={x: image})
    outputFeatureMap(conv2_)
    plt.tight_layout()
    plt.show()
    
    print ()
    print ("Featuremap of conv2 network 5x5x16:")
    conv2_sub_ = sess.run(conv2_sub, feed_dict={x: image})
    outputFeatureMap(conv2_sub_)
    plt.tight_layout()
    plt.show()
    
    print ()
    print ("Featuremap of conv1 network after 2. pooling 7x7x15 (feed to fc0):")
    fc0_conv1_sub_ = sess.run(fc0_conv1_sub, feed_dict={x: image})
    outputFeatureMap(fc0_conv1_sub_)
    plt.tight_layout()
    plt.show()
Featuremap of conv1 network 30x30x15:
Featuremap of conv1 network after pooling 15x15x15:
Featuremap of conv2 network 11x11x16:
Featuremap of conv2 network 5x5x16:
Featuremap of conv1 network after 2. pooling 7x7x15 (feed to fc0):
In [ ]: